[Strengthening causal inference in studies using non-experimental data: an application of propensity score and instrumental variable methods].

OBJECTIVES: This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year.
METHODS: Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively.
RESULTS: While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission.
CONCLUSIONS: This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.